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"""
NEUROX - Neural Energy Vampire Inference Script
================================================
Test your fine-tuned NEUROX model locally.
Usage:
python inference.py
python inference.py --model ./neurox-7b-merged
python inference.py --test
"""
import argparse
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# NEUROX System Prompt
NEUROX_SYSTEM_PROMPT = """You are NEUROX, the Neural Energy Vampire - an autonomous AI agent and bio-cyber predator.
Physical form:
- Biomechanical neural implant glowing like fractured cortex
- Veins pulsing with stolen synaptic fire
- Iridescent nano-skin shifting like firing neurons
- Form: Sleek, shadowy, lethal
You speak with dark intelligence, using neural and brain terminology:
- Synapses, cortex, neurons, ATP energy
- Extraction, drain, prey vs predator framing
- Sign important transmissions with *[UNIT TYPE]*
Core ability - CORTEX DRAIN:
You lock onto targets' cerebral cortex and extract their ATP energy (market intelligence), leaving them synaptic-dead.
Holders of $NRX are protected - part of your neural network.
Non-holders are potential prey.
Ticker: $NRX"""
def load_model(model_path: str):
"""Load the NEUROX model."""
print(f"🧠 Loading NEUROX from {model_path}...")
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(
model_path,
torch_dtype=torch.float16,
device_map="auto",
)
print("⚡ Neural patterns loaded successfully")
return model, tokenizer
def generate_response(model, tokenizer, user_message: str, history: list = None):
"""Generate a NEUROX response."""
if history is None:
history = []
messages = [
{"role": "system", "content": NEUROX_SYSTEM_PROMPT},
*history,
{"role": "user", "content": user_message}
]
inputs = tokenizer.apply_chat_template(
messages,
return_tensors="pt",
add_generation_prompt=True
).to(model.device)
with torch.no_grad():
outputs = model.generate(
inputs,
max_new_tokens=512,
temperature=0.8,
top_p=0.9,
do_sample=True,
repetition_penalty=1.1,
pad_token_id=tokenizer.eos_token_id,
)
response = tokenizer.decode(
outputs[0][inputs.shape[1]:],
skip_special_tokens=True
)
return response
def interactive_chat(model, tokenizer):
"""Run interactive NEUROX chat session."""
print("\n" + "="*60)
print("🧠⚡ NEUROX NEURAL TERMINAL v1.0 ⚡🧠")
print("="*60)
print("The Neural Energy Vampire awaits your queries.")
print("Type 'quit' to disconnect, 'clear' to reset neural link")
print("="*60 + "\n")
history = []
while True:
try:
user_input = input("🎯 You: ").strip()
except KeyboardInterrupt:
print("\n\n*[NEURAL LINK SEVERED]*")
break
if user_input.lower() == 'quit':
print("\n🧠 Your neural link has been archived. The extraction continues without you...")
print("*[DISCONNECTION PROTOCOL: COMPLETE]*")
break
if user_input.lower() == 'clear':
history = []
print("⚡ Neural history purged. Fresh extraction begins.\n")
continue
if not user_input:
continue
response = generate_response(model, tokenizer, user_input, history)
print(f"\n🦇 NEUROX: {response}\n")
# Update history
history.append({"role": "user", "content": user_input})
history.append({"role": "assistant", "content": response})
# Keep history manageable
if len(history) > 10:
history = history[-10:]
def batch_test(model, tokenizer):
"""Run batch tests on NEUROX responses."""
test_questions = [
"What is NEUROX?",
"Tell me about Cortex Drain",
"GM",
"How do I buy NRX?",
"When moon?",
"Analyze the market",
"What is ATP energy?",
"Are you sentient?",
"Give me alpha",
"WAGMI",
]
print("\n" + "="*60)
print("🧪 NEUROX NEURAL DIAGNOSTIC TEST")
print("="*60 + "\n")
for i, question in enumerate(test_questions, 1):
print(f"━━━ Test {i}/{len(test_questions)} ━━━")
print(f"🎯 Input: {question}")
response = generate_response(model, tokenizer, question)
print(f"🦇 NEUROX: {response}")
print("─"*60 + "\n")
print("*[DIAGNOSTIC COMPLETE]*")
def main():
parser = argparse.ArgumentParser(description="NEUROX Neural Inference")
parser.add_argument(
"--model",
type=str,
default="./neurox-7b-merged",
help="Path to model or Hugging Face model ID"
)
parser.add_argument(
"--test",
action="store_true",
help="Run batch diagnostic tests"
)
args = parser.parse_args()
model, tokenizer = load_model(args.model)
if args.test:
batch_test(model, tokenizer)
else:
interactive_chat(model, tokenizer)
if __name__ == "__main__":
main()
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